Vulnerabilities Caused by Metric-based Policies in Reinforcement Learning Based Covert Communication Under Steering Attack

Abstract

This paper explores the concept of timeliness in covert communications when faced with eavesdropping and jamming. We consider a transmitter-receiver pair communicating over a wireless channel where the choice of a resource block (frequency, time) to transmit is the result of a Reinforcement Learning policy. The eavesdropper aims to detect a transmission to perform a steering attack. Using two multiarmed bandit systems, we investigate the problem of minimizing the Age of Information (AoI) regret at the legit receiver, while maximizing the AoI regret at the adversary. We present an upper bound for regret and demonstrate through simulations the validity of the bound and the vulnerabilities introduced by the use of metric-guided policies such as age-aware policies.